用大模型预判表格数据最佳模型，省去大量试错计算。
Pre-Hoc Predictions in AutoML: Leveraging LLMs to Enhance Model Selection and Benchmarking for Tabular datasets
- 基于数据描述和统计信息，用大模型提前筛选候选模型
- 在175个表格数据集上验证，显著减少AutoML搜索空间
- 适合追求高效建模的开发者和研究者
AutoML在后验模型选择方面已取得显著进展，现有工具可自动识别特定数据集上的最优模型。然而，这些方法通常依赖于耗时的超参数遍历，需对多种模型进行训练与测试。相比之下，事前预测提供了一种有前景的替代方案，可通过智能预选模型来跳过耗时的遍历过程。尽管潜力巨大，该方向在文献中仍被严重低估。本文探索了AutoML与事前模型选择的结合，利用传统模型与大型语言模型（LLM）代理，基于数据描述和统计信息缩小AutoML库的搜索空间。该方法应用于AWS AutoGluon基准数据集，包含175个公开于OpenML的表格分类数据集。实验表明，该方法能显著降低计算开销，同时仍可准确选出最佳模型，推动AutoML工作流向更高效的方向演进。
原文摘要 · Abstract (English)
The field of AutoML has made remarkable progress in post-hoc model selection, with libraries capable of automatically identifying the most performing models for a given dataset. Nevertheless, these methods often rely on exhaustive hyperparameter searches, where methods automatically train and test different types of models on the target dataset. Contrastingly, pre-hoc prediction emerges as a promising alternative, capable of bypassing exhaustive search through intelligent pre-selection of models. Despite its potential, pre-hoc prediction remains under-explored in the literature. This paper explores the intersection of AutoML and pre-hoc model selection by leveraging traditional models and Large Language Model (LLM) agents to reduce the search space of AutoML libraries. By relying on dataset descriptions and statistical information, we reduce the AutoML search space. Our methodology is applied to the AWS AutoGluon portfolio dataset, a state-of-the-art AutoML benchmark containing 175 tabular classification datasets available on OpenML. The proposed approach offers a shift in AutoML workflows, significantly reducing computational overhead, while still selecting the best model for the given dataset.
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